The invention relates generally to inspection systems and particularly to pipeline inspection systems that use ultrasound data for inspection of pipe flaws.
Pipelines are widely used in a variety of industries, allowing a large amount of material to be transported from one place to another. A variety of fluids such as oil and/or gas are transported cheaply and efficiently using pipelines. Particulate matter, and other small solids suspended in fluids may also be transported through pipelines. Underground and underwater (deep sea) pipelines typically carry enormous quantities of oil and gas products that are important to energy-related industries, often under high pressure and at extreme temperatures and at high flow rates.
Flaws in constituent pipes may cause pipeline integrity degradation as the pipeline infrastructure ages. Corrosion of a pipeline can be caused by small spots of weakness, subsidence of the soil, local construction projects, seismic activity, weather, and simply wear and tear caused by normal use, and can lead to defects and anomalies in the pipeline. Thus, flaws or defects and anomalies can appear in the surface of the pipeline in the form of corrosion, mechanical damage, fatigue, crack, stress, corrosion cracks, hydrogen induced cracks, or distortion due to dents or wrinkles.
Maintaining and protecting existing pipeline networks is proving to be a challenge. Current state-of-art inline inspection systems use Pipeline Inspection Gages (PIG). These acquire data from multiple sensors while the system travels inside the pipeline. A typical single run for the PIG may be more than 100 km long. The analysis of data and reporting of the findings is semi-automated. Current data analysis methods require on an average, about 200 man-days using ultrasound detection techniques to analyze and evaluate data from a 100 km long pipeline section. These techniques also result in high incidence of false positives because weld signatures (reflections from the weld interfaces of the pipeline) have very similar characteristics as cracks and notches and generally any feature recognition algorithm employed to extract flaws in the pipeline cannot easily distinguish between flaw signatures and weld signatures, which results in a high incidence of false calls.
Therefore there is a need for providing a method for removing weld and noise signatures from the ultrasound scan data obtained by inspecting a pipeline so that flaw signatures are retained for repair/maintenance follow-up activities.